Abstract
Deployment of smart meters has been greatly increased over the recent years. Most of the installed smart meters have been equipped with Advanced Metering Infrastructure (AMI) which enables a bidirectional wireless communication to gather the usage data from gas, electricity and water meters. The insecure wireless channel used by AMI meters jeopardizes the privacy of costumers and brings up cybersecurity issues since it allows hackers to monitor the energy usage data from different houses. To show the penetrability of the system, Received Signal Strength (RSS) - based localization of smart meters incorporating Maximum Likelihood (ML) estimator has been proposed in this paper. By decoding the received signal from a smart meter, one can localize the unoccupied houses or track the people’s daily routines. The effectiveness of the proposed ML location estimator has been examined through MATLAB simulation, under the assumption of a log-normal path loss model and Frequency Shift Keying (FSK) modulation and demodulation. Particle Swarm Optimization (PSO) has been used to find the ML estimation. Finally, the effect of the variance, the number of the sensors and the path loss exponent has been studied on the average Miss Distance Error (MDE).
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
I. H. Cavdar. A solution to remote detection of illegal electricity usage via power line communications. IEEE Transactions on Power Delivery, 19(4):1663–1667, Oct. 2004.
F. Cleveland. Cyber security issues for advanced metering infrastructure (AMI). In Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, 2008.
Aravinthan, Visvakumar, et al. "Wireless AMI application and security for controlled home area networks." Power and Energy Society General Meeting, 2011 IEEE.
E. I. A, U.S. Energy Information Administration, Independent Statistics & Analysis, Available Online: http://www.eia.gov/.
Rouf, Ishtiaq, et al. "Neighborhood watch: Security and privacy analysis of automatic meter reading systems." Proceedings of the 2012 ACM conference on Computer and communications security. ACM, 2012.
Ishtiaq Roufa, et al. "A Practical Study of Security and Privacy Issues in Automatic Meter Reading System." IEEE Spectrum, October 2010.
M. Lisovich and S. Wicker, “Privacy concerns in upcoming residential and commercial demand-response systems,” in 2008 Clemson University Power Systems Conference. Clemson University, 2008.
P. McDaniel and S. McLaughlin, “Security and privacy challenges in the smart grid,” IEEE Security and Privacy, no. 3, pp. 75–77, 2009.
Taylor, R. C. (2013). “Received Signal Strength-Based Localization of Non-Collaborative Emitters in the Presence of Correlated Shadowing” (Doctoral dissertation, Virginia Polytechnic Institute and State University).
Kay, Steven M. “Fundamentals of Statistical signal processing,” Vol 2: Detection theory. Prentice Hall PTR, 1998.
Sargolzaei, Arman, Kang K. Yen, and M.N. Abdelghani. “Time-Delay Switch Attack on Load Frequency Control in Smart Grid.” Advances in Communication Technology, Vol.5 (2013), 55–64.
Sichun Wang, Robert Inkol, and Brad R. Jackson. “Relationship between the maximum likelihood emitter location estimators based on received signal strength (rss) and received signal strength diference (rssd)”. In Communications (QBSC), 2012 26th Biennial Symposium on, pages 64–69.
J. Kennedy and R.C. Eberhart (1995), Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, IEEE Service Center, Piscataway, NJ, 4, pp. 1942–1948.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Jamei, M., Sarwat, A.I., Iyengar, S.S., Kaleem, F. (2015). Security Breach Possibility with RSS-Based Localization of Smart Meters Incorporating Maximum Likelihood Estimator. In: Selvaraj, H., Zydek, D., Chmaj, G. (eds) Progress in Systems Engineering. Advances in Intelligent Systems and Computing, vol 366. Springer, Cham. https://doi.org/10.1007/978-3-319-08422-0_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-08422-0_20
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08421-3
Online ISBN: 978-3-319-08422-0
eBook Packages: EngineeringEngineering (R0)